Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	124,449
2	South Dakota	116,993
3	Utah	95,909
4	Rhode Island	94,969
5	Wisconsin	94,795
6	Tennessee	94,503
7	Iowa	94,379
8	Nebraska	91,413
9	Arizona	86,247
10	Kansas	85,957
11	Arkansas	84,943
12	Oklahoma	84,723
13	Indiana	84,720
14	Idaho	84,248
15	Alabama	82,395
16	Wyoming	81,890
17	Illinois	81,791
18	Nevada	81,321
19	Montana	81,174
20	Mississippi	80,744
21	Minnesota	77,597
22	New Mexico	74,916
23	Louisiana	74,908
24	Missouri	74,052
25	California	70,106
26	Georgia	69,704
27	South Carolina	69,464
28	Florida	69,307
29	Kentucky	69,256
30	Texas	68,686
31	Alaska	68,500
32	Delaware	67,600
33	Ohio	67,152
34	New Jersey	66,443
35	Massachusetts	63,408
36	Colorado	63,349
37	North Carolina	60,353
38	Connecticut	59,843
39	New York	59,116
40	Pennsylvania	57,176
41	West Virginia	57,072
42	Michigan	56,658
43	Maryland	51,255
44	Virginia	47,259
45	District of Columbia	45,331
46	New Hampshire	38,469
47	Washington	36,910
48	Puerto Rico	36,660
49	Oregon	30,017
50	Maine	22,028
51	Hawaii	16,570
52	Vermont	14,548

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Oklahoma	1,235
2	Arizona	1,173
3	California	1,098
4	Rhode Island	941
5	North Carolina	883
6	South Carolina	872
7	Tennessee	859
8	Georgia	856
9	Massachusetts	837
10	New York	828
11	Arkansas	826
12	West Virginia	815
13	Delaware	795
14	Mississippi	744
15	Kentucky	741
16	Indiana	729
17	New Jersey	690
18	Nevada	689
19	Connecticut	688
20	Utah	662
21	Alabama	660
22	Texas	638
23	Ohio	637
24	Florida	610
25	Virginia	604
26	Pennsylvania	591
27	New Mexico	577
28	Kansas	572
29	Maryland	556
30	New Hampshire	528
31	Missouri	498
32	Louisiana	487
33	Illinois	432
34	Wyoming	430
35	Colorado	425
36	Wisconsin	419
37	District of Columbia	418
38	Idaho	369
39	Washington	347
40	South Dakota	346
41	Nebraska	343
42	Alaska	339
43	Montana	339
44	Minnesota	331
45	Iowa	318
46	Maine	298
47	Oregon	297
48	Michigan	272
49	Vermont	245
50	North Dakota	171
51	Puerto Rico	146
52	Hawaii	133

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,244
2	New York	2,025
3	Massachusetts	1,915
4	Rhode Island	1,837
5	Connecticut	1,799
6	South Dakota	1,791
7	North Dakota	1,784
8	Mississippi	1,742
9	Louisiana	1,703
10	Illinois	1,528
11	Michigan	1,420
12	Pennsylvania	1,395
13	Arizona	1,394
14	Arkansas	1,352
15	Indiana	1,339
16	New Mexico	1,319
17	Iowa	1,311
18	District of Columbia	1,163
19	Tennessee	1,141
20	Nevada	1,137
21	South Carolina	1,131
22	Kansas	1,117
23	Alabama	1,090
24	Florida	1,074
25	Texas	1,059
26	Georgia	1,046
27	Maryland	1,042
28	Missouri	1,032
29	Minnesota	1,024
30	Delaware	1,012
31	Montana	988
32	Wisconsin	957
33	Nebraska	942
34	Colorado	918
35	West Virginia	889
36	Idaho	868
37	Wyoming	844
38	Ohio	830
39	California	768
40	North Carolina	727
41	Kentucky	726
42	Oklahoma	701
43	New Hampshire	639
44	Virginia	631
45	Puerto Rico	514
46	Washington	498
47	Utah	436
48	Oregon	385
49	Maine	325
50	Alaska	293
51	Vermont	250
52	Hawaii	216

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arkansas	12
2	Kansas	12
3	Tennessee	12
4	Pennsylvania	11
5	Alabama	10
6	Massachusetts	10
7	Nevada	10
8	South Dakota	10
9	California	9
10	Delaware	9
11	Michigan	9
12	Mississippi	9
13	New York	9
14	Rhode Island	9
15	Arizona	8
16	Connecticut	8
17	New Mexico	8
18	North Carolina	8
19	South Carolina	8
20	Texas	7
21	West Virginia	7
22	Florida	6
23	Illinois	6
24	Indiana	6
25	Louisiana	6
26	New Jersey	6
27	Oklahoma	6
28	District of Columbia	5
29	Minnesota	5
30	New Hampshire	5
31	Puerto Rico	5
32	Colorado	4
33	Georgia	4
34	Idaho	4
35	Maryland	4
36	Nebraska	4
37	Ohio	4
38	Kentucky	3
39	Missouri	3
40	Oregon	3
41	Virginia	3
42	Maine	2
43	Montana	2
44	Wisconsin	2
45	Hawaii	1
46	Iowa	1
47	Utah	1
48	Washington	1
49	Alaska	0
50	North Dakota	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	278,007	1	99
Dewey	South Dakota	227,427	2	99
Lincoln	Arkansas	223,280	3	99
Norton	Kansas	222,160	4	99
Bent	Colorado	220,907	5	99
Davidson	Tennessee	110,543	260	91
Richland	South Carolina	71,125	1591	49
Orange	California	63,856	1950	37
York	South Carolina	63,745	1959	37
Pierce	Washington	34,021	2857	9

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Dickey	North Dakota	6,568	3	99
Grant	Nebraska	6,421	4	99
Gregory	South Dakota	6,213	5	99
Davidson	Tennessee	905	1838	41
Richland	South Carolina	854	1913	39
York	South Carolina	694	2188	30
Orange	California	668	2222	29
Pierce	Washington	428	2608	16

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.